Restoration ecology through the lens of coexistence theory
Authors: Lauren M. Hallett, Lina Aoyama, György Barabás, Benjamin Gilbert, Loralee Larios, Nancy Shackelford, Chhaya M. Werner, Oscar Godoy, Emma R. Ladouceur, Jacob E. Lucero, Christopher P. Weiss-Lehman, Jonathan M. Chase, Chengjin Chu, W. Stanley Harpole, Margaret M. Mayfield, Akasha M. Faist, Lauren G. Shoemaker
Journal: Trends in Ecology & Evolution (2023)

Ecological restoration success can depend on environmental conditions and species interactions, and initial trajectories may not reflect long-term outcomes.Coexistence theory can help diagnose restoration outcomes early by assessing whether focal species can increase when at low density.Partitioning the effect of the environment and competition on the low-density growth rates of focal species can help guide restoration efforts. As human influence over Earth's ecosystems increases and the amount of land available for traditional conservation dwindles, ecological restoration is gaining traction as an essential tool for biodiversity conservation [1.Gann G.D. et al.International principles and standards for the practice of ecological restoration.Restor. Ecol. 2019; 27: S1-S46Crossref Scopus (571) Google Scholar]. Despite enthusiasm, the discipline of restoration ecology has struggled to become a predictive science capable of consistently improving restoration outcomes [2.Hobbs R.J. Norton D.A. Towards a conceptual framework for restoration ecology.Restor. Ecol. 1996; 4: 93-110Crossref Google Scholar,3.Suding K. Toward an era of restoration in ecology: successes, failures, and opportunities ahead.Annu. Rev. Ecol. Evol. Syst. 2011; 42: 465-487Crossref Scopus (233) Google Scholar]. Over the history of restoration ecology, frameworks often assumed a monotonic recovery trajectory [4.Brudvig L.A. The restoration of biodiversity: where has research been and where does it need to go?.Am. J. Bot. 2011; 98: 549-558Crossref PubMed Scopus (202) Google Scholar] or aimed for a 'carbon copy' of a past site [5.Hilderbrand R.H. et al.The myths of restoration ecology.Ecol. Soc. 2005; 10: 19Crossref Scopus (311) Google Scholar], drawing largely from theories of ecological succession and community assembly [6.Wainwright C.E. et al.Links between community ecology theory and ecological restoration are on the rise.J. Appl. Ecol. 2017; 55: 570-581Crossref Scopus (55) Google Scholar]. At the same time, there is long-standing appreciation that restoration trajectories are often nonlinear [7.Young T.P. et al.Community succession and assembly comparing, contrasting and combining paradigms in the context of ecological restoration.Ecol. Restor. 2001; 19: 5-18Crossref Google Scholar,8.Suding K.N. Hobbs R.J. Threshold models in restoration and conservation: a developing framework.Trends Ecol. Evol. 2009; 24: 271-279Abstract Full Text Full Text PDF PubMed Scopus (495) Google Scholar] and outcomes can diverge due to conditions specific to the site and time period of project implementation [9.Choi Y.D. Theories for ecological restoration in changing environment: toward 'futuristic' restoration.Ecol. Res. 2004; 19: 75-81Crossref Scopus (0) Google Scholar,10.Vaughn K.J. Young T.P. Contingent conclusions: year of initiation influences ecological field experiments, but temporal replication is rare.Restor. Ecol. 2010; 18: 59-64Crossref Scopus (65) Google Scholar]. Explicitly incorporating the role of variability in restoration frameworks is important to guide and assess efforts in a variable and changing world [6.Wainwright C.E. et al.Links between community ecology theory and ecological restoration are on the rise.J. Appl. Ecol. 2017; 55: 570-581Crossref Scopus (55) Google Scholar,9.Choi Y.D. Theories for ecological restoration in changing environment: toward 'futuristic' restoration.Ecol. Res. 2004; 19: 75-81Crossref Scopus (0) Google Scholar]. Within the broader field of ecology, Modern Coexistence Theory (hereafter 'coexistence theory') [11.Chesson P. Mechanisms of maintenance of species diversity.Annu. Rev. Ecol. Syst. 2000; 31: 343-366Crossref Scopus (4341) Google Scholar,12.Barabás G. et al.Chesson's coexistence theory.Ecol. Monogr. 2018; 88: 277-303Crossref Scopus (152) Google Scholar] has emerged as a framework to delineate the effect of environment and species interactions on whether and how species coexist, ultimately influencing community composition and diversity. Coexistence theory emphasizes the importance of spatial and temporal variability for community dynamics and provides analytical metrics to assess species success in relation to average and variable conditions. Coexistence theory has led to advances in numerous ecological subfields, such as community ecology [13.Bowler C.H. et al.Accounting for demographic uncertainty increases predictions for species coexistence: a case study with annual plants.Ecol. Lett. 2022; 25: 1618-1628Crossref PubMed Scopus (5) Google Scholar, 14.Hallett L.M. et al.Rainfall variability maintains grass-forb species coexistence.Ecol. 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Modern coexistence theory as a framework for invasion ecology.Biodivers. Sci. 2020; 28: 1362Crossref Scopus (6) Google Scholar]. Here, we unify coexistence theory and restoration ecology to improve restoration goals, strategies, and assessment in an increasingly variable world (Figure 1). We concentrate on restoring biodiversity in herbaceous plant communities, because they are both a frequent focus of ecological restoration and a model system for developments in coexistence theory. However, our proposed approaches and principles are adaptable to other systems. Coexistence theory is a framework for understanding and delineating common coexistence mechanisms (see Glossary) that maintain species coexistence [11.Chesson P. Mechanisms of maintenance of species diversity.Annu. Rev. Ecol. Syst. 2000; 31: 343-366Crossref Scopus (4341) Google Scholar]. These include classic niche partitioning as well as mechanisms that depend on environmental fluctuations. For example, coexistence theory has formalized that differences in the responses of species to environmental fluctuations can lead to temporal niche partitioning when species are able to 'store' through bad years, such as through seed banks or adult stages, a component of the commonly considered 'storage effect' mechanism [12.Barabás G. et al.Chesson's coexistence theory.Ecol. Monogr. 2018; 88: 277-303Crossref Scopus (152) Google Scholar,21.Sears A.L. Chesson P. New methods for quantifying the spatial storage effect: an illustration with desert annuals.Ecology. 2007; 88: 2240-2247Crossref PubMed Scopus (100) Google Scholar]. Similarly, temporal fluctuations in the abiotic environment or biotic interactions that lead to greater benefits in good years relative to bad years tend to promote species persistence [22.Chesson P. Quantifying and testing coexistence mechanisms arising from recruitment fluctuations.Theor. Popul. Biol. 2003; 64: 345-357Crossref PubMed Scopus (0) Google Scholar, 23.Letten A.D. et al.Species coexistence through simultaneous fluctuation-dependent mechanisms.Proc. Natl. Acad. Sci. U. S. A. 2018; 115: 6745-6750Crossref PubMed Scopus (64) Google Scholar, 24.Shoemaker L.G. et al.Integrating the underlying structure of stochasticity into community ecology.Ecology. 2020; 101e02922Crossref PubMed Scopus (67) Google Scholar]. A key contribution is that coexistence theory provides analytical tools to quantify both coexistence and the contributing role of environmental variability. How can we assess whether a species will persist or go extinct over time? Persistence is commonly assessed via the invasion criterion, whereby species can coexist if they can each increase from low density while experiencing species interactions from the surrounding resident community [11.Chesson P. Mechanisms of maintenance of species diversity.Annu. Rev. Ecol. Syst. 2000; 31: 343-366Crossref Scopus (4341) Google Scholar,25.Chesson P. Updates on mechanisms of maintenance of species diversity.J. Ecol. 2018; 106: 1773-1794Crossref Scopus (128) Google Scholar]. The invasion criterion is evaluated by calculating the low-density growth rate (LDGR) of each focal species; a positive LDGR indicates that the focal species can persist, while a negative LDGR indicates a species cannot invade or, if present, is predicted to eventually go locally extinct [11.Chesson P. Mechanisms of maintenance of species diversity.Annu. Rev. Ecol. Syst. 2000; 31: 343-366Crossref Scopus (4341) Google Scholar,12.Barabás G. et al.Chesson's coexistence theory.Ecol. Monogr. 2018; 88: 277-303Crossref Scopus (152) Google Scholar]. As such, the LDGR reflects the joint influence of the intrinsic growth of the focal species in the absence of interactions, and the net impact of interactions with the resident community [12.Barabás G. et al.Chesson's coexistence theory.Ecol. Monogr. 2018; 88: 277-303Crossref Scopus (152) Google Scholar]. Long-term persistence is assessed by averaging the LDGR over periods that capture the full range of environmental variability that the species experiences [14.Hallett L.M. et al.Rainfall variability maintains grass-forb species coexistence.Ecol. Lett. 2019; 22: 1658-1667Crossref PubMed Scopus (52) Google Scholar]. The use of LDGR to assess coexistence is helpful but imperfect; as one example, it fails to capture scenarios in which the growth of a species depends on the presence of conspecifics (e.g., Allee effects) [12.Barabás G. et al.Chesson's coexistence theory.Ecol. Monogr. 2018; 88: 277-303Crossref Scopus (152) Google Scholar]. However, a focus on low-density growth generally reflects realistic field conditions, because most populations experience periods of rarity, especially within small restoration sites and/or diverse communities [26.Maina G.G. Howe H.F. Inherent rarity in community restoration.Conserv. Biol. 2000; 14: 1335-1340Crossref Scopus (41) Google Scholar]. Does environmental variability help species persist or increase their risk of extinction? Coexistence theory addresses this question through growth rate partitioning, which accounts for the variation that occurs in time [27.Chesson P. Multispecies competition in variable environments.Theor. Popul. Biol. 1994; 45: 227-276Crossref Scopus (480) Google Scholar] or space [28.Chesson P. General theory of competitive coexistence in spatially-varying environments.Theor. Popul. Biol. 2000; 58: 211-237Crossref PubMed Scopus (636) Google Scholar]. Different partitionings have been developed to target different dynamics [22.Chesson P. Quantifying and testing coexistence mechanisms arising from recruitment fluctuations.Theor. Popul. Biol. 2003; 64: 345-357Crossref PubMed Scopus (0) Google Scholar,27.Chesson P. Multispecies competition in variable environments.Theor. Popul. Biol. 1994; 45: 227-276Crossref Scopus (480) Google Scholar, 28.Chesson P. General theory of competitive coexistence in spatially-varying environments.Theor. Popul. Biol. 2000; 58: 211-237Crossref PubMed Scopus (636) Google Scholar, 29.Adler P.B. et al.A niche for neutrality.Ecol. Lett. 2007; 10: 95-104Crossref PubMed Scopus (780) Google Scholar, 30.Ellner S.P. et al.An expanded modern coexistence theory for empirical applications.Ecol. Lett. 2019; 22: 3-18Crossref PubMed Scopus (72) Google Scholar], but the common idea is that the LDGR is written as a sum of terms, each reflecting a mechanism by which variability alters coexistence. For example, in a partitioning developed by Ellner et al. [30.Ellner S.P. et al.An expanded modern coexistence theory for empirical applications.Ecol. Lett. 2019; 22: 3-18Crossref PubMed Scopus (72) Google Scholar], simulations are conducted in which variability is turned 'on' or 'off' for each component of the population model of a species (such as intrinsic growth rate and competition coefficients), singly and in combination, to assess the importance of variability in each parameter for the overall LDGR of the species [14.Hallett L.M. et al.Rainfall variability maintains grass-forb species coexistence.Ecol. Lett. 2019; 22: 1658-1667Crossref PubMed Scopus (52) Google Scholar,23.Letten A.D. et al.Species coexistence through simultaneous fluctuation-dependent mechanisms.Proc. Natl. Acad. Sci. U. S. A. 2018; 115: 6745-6750Crossref PubMed Scopus (64) Google Scholar]. This is especially helpful for restoration scenarios, because it isolates the effect of environmental variability on the intrinsic growth of a species versus the competition they experience. In a similar vein, simulations can be conducted to partition the consequence of different restoration strategies (such as modifying the environment versus the resident community) on the persistence and abundance of focal species (Box 1).Box 1Applying coexistence models to restoration scenariosCoexistence models can be used to assess restoration success and make predictions about the outcomes of different restoration interventions. To be realistic, these models should describe the performance of a species over the range of environmental and competitive conditions it experiences (Figure IA,B). Site descriptions, including long-term climate records, soil maps, vegetation monitoring, natural history, and practitioner experience, can inform the range of conditions to include. A common approach is to fit a population model for the focal species under each representative environmental condition. This can be done both experimentally, by creating different environmental conditions and manipulating the densities of focal and resident species within them, and observationally, using monitoring data that capture a sufficient range of environmental and competitive scenarios. The yield of the focal species (e.g., biomass or fecundity) should be measured, as well as the abundance of the resident community. While the functional form of the population model may vary by system, these measures allow both the intrinsic growth rate of the focal species' (λ) and the effect of neighboring species (α) to be estimated for each environmental condition (Figure IC,D).Once models are fit, the LDGR can be calculated through a simulation in which the focal species is introduced at low density to the existing resident community. In variable environments, long-term persistence can be predicted by calculating the average LDGR using parameters associated with each environmental condition, weighted by the frequency with which they occur. Similarly, the role of variability on average LDGR can be partitioned through simulations in which each parameter (e.g., λ, α) either varies with the environment or is held constant, singly and in combination (Figure IE). The role of different restoration interventions can likewise be simulated by either altering the distribution of environmental conditions used to calculate the average LDGR of the focal species (to reflect restoration actions that ameliorate site conditions) or by reducing the density of the resident community before calculating average LDGR (to reflect restoration actions that reduce competitors) (Figure IE). Finally, the long-term abundance of the focal species can be predicted through simulations in which growth at each time step is calculated using parameters associated with either observed or targeted environmental conditions. While a benefit of the LDGR is that it only requires modeling the focal species, abundance predictions are improved by reciprocally modeling the residents, especially when the focal species substantially affects them. Coexistence models can be used to assess restoration success and make predictions about the outcomes of different restoration interventions. To be realistic, these models should describe the performance of a species over the range of environmental and competitive conditions it experiences (Figure IA,B). Site descriptions, including long-term climate records, soil maps, vegetation monitoring, natural history, and practitioner experience, can inform the range of conditions to include. A common approach is to fit a population model for the focal species under each representative environmental condition. This can be done both experimentally, by creating different environmental conditions and manipulating the densities of focal and resident species within them, and observationally, using monitoring data that capture a sufficient range of environmental and competitive scenarios. The yield of the focal species (e.g., biomass or fecundity) should be measured, as well as the abundance of the resident community. While the functional form of the population model may vary by system, these measures allow both the intrinsic growth rate of the focal species' (λ) and the effect of neighboring species (α) to be estimated for each environmental condition (Figure IC,D). Once models are fit, the LDGR can be calculated through a simulation in which the focal species is introduced at low density to the existing resident community. In variable environments, long-term persistence can be predicted by calculating the average LDGR using parameters associated with each environmental condition, weighted by the frequency with which they occur. Similarly, the role of variability on average LDGR can be partitioned through simulations in which each parameter (e.g., λ, α) either varies with the environment or is held constant, singly and in combination (Figure IE). The role of different restoration interventions can likewise be simulated by either altering the distribution of environmental conditions used to calculate the average LDGR of the focal species (to reflect restoration actions that ameliorate site conditions) or by reducing the density of the resident community before calculating average LDGR (to reflect restoration actions that reduce competitors) (Figure IE). Finally, the long-term abundance of the focal species can be predicted through simulations in which growth at each time step is calculated using parameters associated with either observed or targeted environmental conditions. While a benefit of the LDGR is that it only requires modeling the focal species, abundance predictions are improved by reciprocally modeling the residents, especially when the focal species substantially affects them. Restoration goals are often centered on achieving a desired community composition, and success has historically been assessed by comparing the abundance of target species in restored versus reference communities (Figure 1A [1]). However, after management, many species are still in flux, experiencing transient dynamics that are not indicative of long-term persistence [31.Guerrant E.O. The value and propriety of reintroduction as a conservation tool for rare plants.Botany. 2013; 91: v-xCrossref Scopus (21) Google Scholar,32.Shriver R.K. Transient population dynamics impede restoration and may promote ecosystem transformation after disturbance.Ecol. Lett. 2019; 22: 1357-1366Crossref PubMed Scopus (47) Google Scholar] but rather representing successional stages [33.del Moral R. et al.Insights gained from succession for the restoration of landscape structure and function.in: Walker L.R. Linking Restoration and Ecological Succession. Springer, 2007: 19-44Crossref Google Scholar] or restoration actions, such as planting or seeding [34.Aoyama L. et al.Application of modern coexistence theory to rare plant restoration provides early indication of restoration trajectories.Ecol. Appl. 2022; 32e2649Crossref PubMed Scopus (2) Google Scholar]. These dynamics, paired with the short monitoring windows of many projects [3.Suding K. Toward an era of restoration in ecology: successes, failures, and opportunities ahead.Annu. Rev. Ecol. Evol. Syst. 2011; 42: 465-487Crossref Scopus (233) Google Scholar], limit our ability to link abundance patterns to long-term persistence dynamics in restoration [35.Zurell D. et al.Spatially explicit models for decision-making in animal conservation and restoration.Ecography. 2022; 4e05787Google Scholar]. A coexistence theory approach can reconcile discrepancies between early indicators of success and long-term persistence for species [32.Shriver R.K. Transient population dynamics impede restoration and may promote ecosystem transformation after disturbance.Ecol. Lett. 2019; 22: 1357-1366Crossref PubMed Scopus (47) Google Scholar,36.Armstrong D.P. Seddon P.J. Directions in reintroduction biology.Trends Ecol. Evol. 2008; 23: 20-25Abstract Full Text Full Text PDF PubMed Scopus (756) Google Scholar]. The invasion criterion provides a clear restoration goal: a positive LDGR for each focal species, which puts the focus on long-term population persistence, while not precluding abundance-based metrics of biodiversity as additional gauges of restoration success (Table 1). This is most appropriate when the focal species has minimal effect on the resident community or when its effect is aligned with ancillary restoration goals, such as a competitive effect on an undesirable resident. For the restoration of species of conservation concern, single-population analyses can be sufficient [37.Albrecht M.A. et al.Effects of life history and reproduction on recruitment time lags in reintroductions of rare plants.Conserv. Biol. 2019; 33: 601-611Crossref PubMed Scopus (24) Google Scholar,38.Vitt P. et al.Effects of community-level grassland management on the non-target rare annual Agalinis auriculata.Biol. Conserv. 2009; 142: 798-805Crossref Scopus (19) Google Scholar]. This approach deviates from traditional coexistence theory in that the focus is on the LDGR of the species of interest rather than on the reciprocal LDGR of all species in the community, which greatly reduces data requirements. However, in the case of diverse communities, goals could delineate how many target species are expected to persist, with the persistence of each species quantified separately [39.Bowler C.H. et al.Positive effects of exotic species dampened by neighborhood heterogeneity.Ecology. 2022; 103e3779Crossref PubMed Scopus (0) Google Scholar], so long as the primary concern is how the focal species interact with the existing resident community rather than with one another [19.Kraft N.J. et al.Plant functional traits and the multidimensional nature of species coexistence.Proc. Natl. Acad. Sci. U. S. A. 2015; 112: 797-802Crossref PubMed Google Scholar,40.Van Dyke M.N. et al.Small rainfall changes drive substantial changes in plant coexistence.Nature. 2022; 611: 507-511Crossref PubMed Scopus (8) Google Scholar]. Finally, a coexistence theory approach can be used to develop goals for nondesired species, such as aiming for nondesirable species to have negative growth rates, leading to local elimination.Table 1Information required to apply coexistence theory in diverse restoration situationsRestoration goalInformationSituationRefsIncrease plant cover/biomassSpecies-level variation in cover/biomass under different environmental conditions from bare patches to full coverAfter mining events, constructing roads or other actions that entirely remove plant cover[90.Cobbaert D. et al.Experimental restoration of a fen plant community after peat mining.Appl. Veg. Sci. 2004; 7: 209-220Crossref Scopus (66) Google Scholar,91.Miao Z.-W. et al.Ecological rebuilding and land reclamation in surface mines in Shanxi province, China.J. Environ. Sci. 2000; 12: 486-497Google Scholar]Increase keystone speciesVariation in keystone species performance across environmental conditions when grown alone versus when grown in competition with itself or with all other speciesAfter loss of keystone species due to fire, flooding or other strong perturbation, or following introduction of a keystone species that has dramatically altered the ecosystem[92.Layton C. et al.Kelp forest restoration in Australia.Front. Mar. Sci. 2020; 7: 74Crossref Scopus (84) Google Scholar,93.Koch J.M. Samsa G.P. Restoring jarrah forest trees after bauxite mining in Western Australia.Restor. Ecol. 2007; 15: S17-S25Crossref Scopus (47) Google Scholar]Remove undesired of invasive speciesVariation in invasive species performance across environmental conditions when grown alone versus when grown in competition with itself or with all other speciesAfter an invasion event due to a new introduction or to changes in environmental conditions that indirectly allows an invasion, such as human disturbance or nitrogen deposition[34.Aoyama L. et al.Application of modern coexistence theory to rare plant restoration provides early indication of restoration trajectories.Ecol. Appl. 2022; 32e2649Crossref PubMed Scopus (2) Google Scholar,94.Flory S.L. Clay K. Invasive plant removal method determines native plant community responses.J. Appl. Ecol. 2009; 46: 434-442Crossref Scopus (0) Google Scholar]Increase functional biodiversityVariation in the performance of each functional group separately across environmental conditions. Differences in performance should distinguish the effect of environmental conditions from the effect of the density of other functional groups (including itself)After a perturbation event that has particularly affected some functional groups. These perturbations can include pest outbreak, nutrient deposition, or extreme events, such as heat waves[95.Gherardi L.A. Sala O.E. Enhanced interannual precipitation variability increases plant functional diversity that in turn ameliorates negative impact on productivity.Ecol. Lett. 2015; 18: 1293-1300Crossref PubMed Scopus (94) Google Scholar,96.Ostertag R. et al.Using plant functional traits to restore Hawaiian rainforest.J. Appl. Ecol. 2015; 52: 805-809Crossref Scopus (96) Google Scholar]Increase species richnessThe most data-demanding restoration goal because it should account for the performance of each individual species across independent axes of environmental conditions and species densitiesRestoration goals aiming to increase or mitigate negative effects of global change drivers on biodiversity, such as climate change, invasive species, or land use intensity[97.Allan E. et al.Interannual variation in land-use intensity enhances grassland multidiversity.Proc. Natl. Acad. Sci. U. S. A. 2014; 111: 308-313Crossref PubMed Scopus (202) Google Scholar,98.Pakeman R.J. Functional diversity indices reveal the impacts of land use intensification on plant community assembly.J. Ecol. 2011; 99: 1143-1151Crossref Scopus (161) Google Scholar] Open table in a new tab To date, the use of variability in setting goals has generally relied on quantifying historic ranges of variability within target ecosystems, a data-intensive exercise (e.g., [41.Farrell H.L. et al.The effects of site preparation equal those of seeding at a dryland restoration site: 6 years of plant community development.Restor. Ecol. 2021; 29e13482Crossref PubMed Scopus (4) Google Scholar]) that does not explicitly link community fluctuations to specific environmental variables. By contrast, a coexistence theory approach focuses on when and how the viability of a species shifts in variable environments [14.Hallett L.M. et al.Rainfall variability maintains grass-forb species coexistence.Ecol. Lett. 2019; 22: 1658-1667Crossref PubMed Scopus (52) Google Scholar,42.Mordecai E.A. et al.Controls over native perennial grass exclusion and persistence in California grasslands invaded by annuals.Ecology. 2015; 96: 2643-2652Crossref PubMed Scopus (15) Google Scholar,43.Bimler M.D. Accurate predictions of coexistence in natural systems require the inclusion of facilitative interactions and environmental dependency.J. Ecol. 2018; 106: 1839-1852Crossref Scopus (59) Google Scholar], informing more quantifiable restoration goals (Table 1). For example, Usinowicz and Levine [44.Usinowicz J. Levine J.M. Species persistence under climate change: a geographical scale coexistence problem.Ecol. Lett. 2018; 21: 1589-1603Crossref PubMed Scopus (22) Google Scholar] mapped how LDGRs vary along a climate gradient and used this to forecast population viability under climate change, an approach that could be used to set realistic goals for current versus future climate conditions. Improving restoration actions are at the heart of restoration ecology, with science ideally helping to address such questions as: what sites have the most potential for restoration success [45.Wilson K.A. et al.Optimal restoration: accounting for space, time and uncertainty.J. Appl. Ecol. 2011; 48: 715-725Crossref Scopus (100) Google Scholar]? Is passive restoration (i.e., simply ceasing the causes of degradation) sufficient, or is active restoration necessary [46.Holl K.D. Aide T.M. When and where to actively restore ecosystems?.For. Ecol. Manag. 2011; 261: 1558-1563Crossref Scopus (420) Google Scholar]? If active restoration is needed, should restoration target the abiotic conditions of the site, its biotic community, or both [47.Hobbs R.J. et al.Intervention ecology: applying ecological science in the twenty-first century.BioScience. 2011; 61: 442-450Crossref Scopus (303) Google Scholar]? To what degree are these outcomes influenced by temporal environmental variability [7.Young T.P. et al.Community succession and assembly comparing, contrasting and combining paradigms in the context of ecological restoration.Ecol. Restor. 2001; 19: 5-18Crossref Google Scholar,34.Aoyama L. et al.Application of modern coexistence theory to rare plant restoration